The team assembled its benchmark sample from an unusual and valuable archive: special warnings issued by the Peruvian Navy between 2008 and 2025. These operational records classify swell episodes by their expected coastal impact, with a very strong swell defined as waves exceeding three times their normal height and a strong swell between two and three times normal. From this record, the researchers identified five very strong events, all arriving during the austral winter months of May through September with pronounced south-westerly directions from the South Pacific, and six strong events originating in the North Pacific, mostly during boreal winter. Because the Navy archive is the only homogeneous operational classification available for the Peruvian coast, the authors treat these eleven cases as a benchmark sample rather than an exhaustive climatology.

A central challenge in studying remote swells is timing. The waves that batter a Peruvian beach were generated days earlier, sometimes on the far side of an entire ocean basin, so the atmospheric conditions that matter occurred long before the coastal impact. The researchers solved this by applying deep-water wave theory, calculating the group velocity of a swell packet from its wave period and the great-circle distance between the storm’s wind region and the coast. Using representative swell periods of 16 to 20 seconds, consistent with long-period swells previously reported for Peru, they estimated travel times of roughly three to four days for Southern Hemisphere events and eight to eleven days for Northern Hemisphere events. These windows then defined the periods over which they examined atmospheric fields from the ERA5 reanalysis.

The composite analysis of the Southern Hemisphere events revealed a strikingly organized pattern. Days before the swell peak, sea-level pressure anomalies form a tripolar structure over the southeast Pacific: a deep extratropical cyclone south of 40 degrees south, flanked by broad anticyclonic anomalies to its southwest and southeast. This arrangement dramatically tightens the meridional pressure gradient along the storm’s equatorward flank, driving a wide corridor of intensified westerly and south-westerly winds between roughly 35 and 55 degrees south. Crucially, the disturbance is barotropic, meaning the pressure anomalies at the surface, at 500 hectopascals, and at 250 hectopascals are nearly vertically aligned, allowing the storm to persist and sustain its winds over the same stretch of ocean. The result is a swath of enhanced significant wave height exceeding one meter across the high-latitude source region, with wave energy radiating directly toward Peru and Chile.

The jet stream diagnostics added a decisive layer to the picture. Applying a multiparametric method that measures not just jet intensity but also sharpness, tilt, and meridional coherence, the team found that Southern Hemisphere events coincide with a polar-front jet that is significantly stronger and sharper than the austral-winter climatology, with its core confined to a narrow latitude band and a modest southwest-to-northeast tilt. This compact, energized jet reinforces the storm-jet coupling that keeps the surface wind corridor locked in place over the swell-generation region. In the Northern Hemisphere, the configuration is different but equally coherent: the subtropical jet intensifies, sharpens, and extends longitudinally across the North Pacific, while the polar-front jet weakens and becomes less continuous. This redistribution of upper-level momentum supports deep, slow-moving cyclones over the central and western North Pacific, whose westerly to north-westerly winds can still fling wave energy across the Equator toward northern Peru.

The hemispheric contrast also explains why Southern Hemisphere events hit harder. Swell from the South Pacific travels a shorter path, arriving in three to four days with limited dispersion, and the tripolar pressure pattern sustains winds over an exceptionally long fetch. Northern Hemisphere swells must cross the entire Pacific and the Equator, a journey of eight to eleven days during which the wave energy spreads and attenuates. That is why, in the Navy’s classification, Southern Hemisphere events reached the very strong category while their Northern counterparts topped out at strong. Recent episodes illustrate the stakes: a May 2023 anomalous southwest swell caused coastal flooding and closed dozens of Peruvian ports, while a late-December 2024 swell from a North Pacific winter storm surprised tourist beaches and small ports on Peru’s northern coast under otherwise fair local weather.

Perhaps the most consequential finding concerns climate change. Because extreme swell records span only a few years, the team turned to a flow-analogue framework, searching the ERA5 record from 1950 to 2025 for days with atmospheric circulations dynamically similar to each event and comparing the surface winds associated with those analogues in past and present climate periods. Because circulation is held fixed across analogues, any difference in wind speed cannot be attributed to changes in the circulation pattern itself. For the Southern Hemisphere events, the result was unambiguous: analogous circulation patterns are now associated with significantly stronger surface winds than in the past, and the corresponding significant wave heights have increased as well. The one exception was event E3, which showed no significant influence of the dominant climate mode but may instead respond to the regional forcing of the Amundsen Sea Low.

The likely culprit behind this Southern Hemisphere intensification is the Southern Annular Mode, the leading mode of extratropical Southern Hemisphere variability. Analogue days in the recent period occurred predominantly under positive SAM phases, which strengthen and shift the westerly wind belt poleward, boosting windiness over the high-latitude South Pacific exactly where the swell-generating storms live. The trend toward more positive SAM conditions has been linked in the scientific literature to stratospheric ozone depletion and rising greenhouse gas concentrations, providing a plausible physical pathway from anthropogenic forcing to stronger swell-producing winds. The authors are careful to note that differences between past and present analogues may also reflect internal climate variability or other external factors, but the coherence of the signal across nearly all Southern Hemisphere events, supported by statistically significant wave-height increases, makes it physically compelling.

The Northern Hemisphere story is far messier. Analogue reconstructions revealed no consistent climate-change signal: the three most recent events showed stronger present-day winds, while the three earlier ones showed weaker winds, and no single mode of Pacific variability, whether the Pacific Decadal Oscillation, ENSO, the Pacific-North American pattern, the Arctic Oscillation, the Western Pacific pattern, or the Interdecadal Pacific Oscillation, could account for the split. The two groups of events appear governed by opposing configurations of large-scale climate modes, and regression analysis found these modes explain only a modest fraction of the wind variability, implying that local synoptic processes and factors such as aerosol changes, Arctic sea-ice variations, and subtropical tropospheric warming may also be at play. The high interannual and decadal variability of the North Pacific, the authors note, is precisely why the IPCC expresses low confidence in attributing observed changes in extratropical cyclones there to human influence.

The practical payoff of this work lies in early warning. Because the precursor patterns, a deep barotropic cyclone aligned with an energized jet, emerge days before coastal impact, monitoring these large-scale configurations could give Peruvian authorities substantially longer lead times than current approaches, allowing ports to close, fishing fleets to stay ashore, and coastal communities to prepare before the waves arrive. The authors caution that their sample is concentrated after 2010 and that extending the analysis further back will require reconstructing pre-2008 events. They also propose applying the flow-analogue framework to large ensembles of climate model simulations under historical and future forcing scenarios, which would allow a formal attribution of the circulation changes and a more robust assessment of how anthropogenic climate change will shape the swell hazards of Peru’s coastline in the decades ahead.

Subject of Research: Meteorological and jet-stream drivers of extreme remote swell events affecting the Peruvian coast

Article Title: Meteorological drivers of extreme swells on the Peruvian coast

Article References: Meteorological drivers of extreme swells on the Peruvian coast. (n.d.). https://doi.org/10.5194/wcd-7-1779-2026

Image Credits: AI Generated

DOI: 10.5194/wcd-7-1779-2026

Keywords: extreme swells, Peru, jet stream, extratropical cyclones, ERA5 reanalysis, Southern Annular Mode, Pacific Decadal Oscillation, coastal flooding, wave climate, flow analogues, climate change attribution, early warning systems

The stakes are far from abstract. France’s offshore wind sector is projected to reach a cumulative capacity of 3.6 gigawatts by the end of 2027, driven by the commissioning of seven major projects. These turbines and their foundations are engineered to survive more than two decades in one of the most hostile environments on Earth, and their designs rest on a deceptively simple statistical assumption: that the statistics of the ocean, including the mean, the variance, and above all the extremes of wave height, remain stationary over time. Climate change breaks that assumption. Rising sea levels, migrating storm tracks, and shifting seasonal patterns mean that the wave climate a turbine faces in 2080 may be fundamentally different from the one measured during the historical record used to certify it.

At the heart of the study lies a class of statistical tools known as generalized extreme value, or GEV, models. These models describe the distribution of extreme values, such as the maximum wave height recorded in a given block of time, using three parameters: a location parameter that sets the central tendency of the extremes, a scale parameter that controls their spread, and a shape parameter that governs the behavior of the distribution’s tail, which determines how catastrophic the rarest events can become. Traditionally, engineers fit a GEV distribution to annual maxima, extracting the so-called 100-year return level, the wave height expected to be exceeded on average once per century. But annual maxima throw away most of the data, leaving only one point per year and producing wide, often impractically large, uncertainty bands.

Raillard and his team took a different route. Instead of annual maxima, they modeled monthly maxima, which multiplies the available data by a factor of twelve. The catch is that monthly maxima are not identically distributed, because wave climate is strongly seasonal. To handle this, the researchers turned to Generalized Additive Models, allowing the location and scale parameters of the GEV distribution to vary smoothly across the months of the year using spline functions, flexible curves fitted to the data. The shape parameter, notoriously difficult to estimate, was held constant. From the fitted monthly distributions, the team reconstructed the distribution of annual maxima by multiplying together the twelve monthly cumulative distribution functions, an operation justified by statistical tests showing no remaining time dependence in the model residuals once seasonality was accounted for.

The framework then went a step further into genuinely non-stationary territory. The researchers extended their model so that the seasonal cycles themselves evolve over time, using spline surfaces defined over the joint space of month and year. Cyclic P-splines captured the annual seasonality, while thin plate regression splines captured its long-term drift. Fitted independently to each of eight global climate models from the CMIP6 ensemble, under both a low-emission scenario (SSP1-2.6) and a high-emission scenario (SSP5-8.5), the model could shrink gracefully to a stationary form wherever the data showed no evidence of change, while flexibly tracking complex, non-linear evolutions elsewhere. Model selection was performed using the Akaike Information Criterion, and uncertainty was quantified through a Monte Carlo procedure simulating model parameters one thousand times.

The wave data themselves came from a numerical wave model, WaveWatch III, forced by the eight CMIP6 general circulation models, covering a historical period from 1985 to 2014 and a future window from 2071 to 2100 at three-hour temporal and half-degree spatial resolution. Because global climate models are too coarse to represent local conditions accurately, the team applied a statistical bias correction known as the CDF-t method, which maps the cumulative distribution of the global model onto that of a high-resolution local reanalysis, HYWAT for the English Channel and Atlantic and MED-WAV for the Mediterranean. The correction was applied month by month at six representative offshore sites chosen to span the diversity of the French seafront, and it markedly tightened the scatter of the climate models around the reanalysis reference.

The results paint a vivid picture of a changing wave climate. At the representative site in the Eastern English Channel, historical significant wave heights, the average height of the highest third of waves, ranged roughly between 3 and 7 meters. Under the high-emission scenario, that range widens to about 2 to 8 meters, with winter extremes pushing toward 8 meters and summer minima dipping near 2 meters. Perhaps most striking is the temporal redistribution: in most models the calm summer period grows longer and peaks later in the year, with the lowest wave heights shifting from June toward July, and January extremes exceeding December values by 0.2 to 0.5 meters. The seasonal cycle itself appears to be migrating toward the end of the year, hinting that conventional definitions of the seasons may need revision in a warming ocean.

When the researchers translated these shifts into design quantities, the ensemble average of the 100-year return level rose from approximately 6.15 meters in the historical period to 6.75 meters under SSP1-2.6 and 7 meters under SSP5-8.5 at the Eastern Channel site. Across all seafronts, the equivalent lifetime design level, the wave height that should not be exceeded over the operational life of a wind farm, increased almost everywhere: from about 7.4 meters historically to 8.2 meters under the high-emission scenario in the English Channel, and from 11 to 13 meters to 13 to 15 meters along the Atlantic coast. The North Atlantic site showed a clear increase of roughly 2.5 meters. The Mediterranean told a messier story, with weaker seasonal signals, much larger inter-model spread, and some physically implausible outliers, reaching simulated wave heights of up to 20 meters with uncertainties of 30 meters, likely reflecting coarse model resolution and poorly resolved coastal dynamics in that enclosed basin.

Methodologically, the payoff of using monthly maxima was substantial. Because the model is fitted to twelve times more data, the confidence intervals around the return levels narrowed dramatically compared with the classical annual-maxima approach, which the authors note can yield spurious results when data are scarce. Compared with an earlier study reporting an uncertainty of 13.15 meters for the 100-year return level under a comparable scenario, the new method achieved a maximum uncertainty of about 10 meters, a reduction the authors attribute to the larger effective sample size. The team also introduced a new definition of the design condition itself: rather than a fixed 100-year return level, they computed the quantile of the maximum wave height over the entire lifetime of the structure, calibrated so that the cumulative probability of failure over, say, thirty years matches the conventional stationary target.

The implications ripple outward from engineering offices to energy markets. Under-designed structures face reduced operational lifespans, escalating maintenance costs, and, in the worst case, catastrophic failure, while over-conservative designs inflate capital expenditure unnecessarily. By providing a statistically rigorous, uncertainty-aware way to translate climate projections into design loads, the framework offers engineers, designers, and stakeholders a tool for building offshore wind farms that remain both safe and economically viable across decades of environmental change. The authors see their work as a starting point rather than an endpoint, pointing toward refined wave modeling along the French coastline, parametric treatments of evolving seasonality, and joint analyses of wave height and peak period under future climates. As the ocean’s moods continue to shift, the mathematics used to tame them is shifting with it.

Subject of Research: Non-stationary extreme value analysis of projected changes in significant wave height for offshore wind farm design along the French coasts

Article Title: Non-stationary GEV models for estimating design sea-states in a changing climate – applications to offshore wind farms along the French coasts

Article References: Non-stationary GEV models for estimating design sea-states in a changing climate – applications to offshore wind farms along the French coasts. (n.d.). https://doi.org/10.5194/ascmo-12-195-2026

Image Credits: AI Generated

DOI: 10.5194/ascmo-12-195-2026

Keywords: offshore wind, significant wave height, GEV distribution, non-stationary extremes, CMIP6, climate change, return levels, French coasts, English Channel, Mediterranean Sea, extreme value analysis, wave climate

The study, carried out by Axel Hidalgo-Mayo of the Institute of Meteorology in Havana, Cuba, builds its climatology on the ERA5 reanalysis, a state-of-the-art reconstruction of past weather produced by the Copernicus Climate Change Service. Reanalysis products combine historical observations with a numerical weather model to produce a physically consistent record of winds and waves stretching back decades. To make sure the reanalysis could be trusted in this particular basin, the author validated it against eight wave-measuring buoys operated by the United States National Data Buoy Center. The agreement was strong: the median Spearman correlation between modeled and observed significant wave height reached 0.928, and the Perkins Skill Score, a measure of how well the full statistical distribution of wave heights matches observations, averaged 91.6 percent. Those numbers matter, because every conclusion about long-term trends rests on the fidelity of the underlying record.

Significant wave height, abbreviated Hs, is the standard metric oceanographers use to characterize sea state; it corresponds roughly to the average height of the highest one-third of waves and is the quantity engineers use when designing platforms, breakwaters and coastal defenses. Rather than treating the Gulf of Mexico and the Caribbean Sea as a single homogeneous basin, the study identifies three physically distinct wave regimes. The eastern Caribbean is sustained almost continuously by the Caribbean Low-Level Jet, a persistent river of fast-moving air that funnels through the mountain passes of Central America and blows steadily across the basin, producing waves with a coefficient of variation of just 0.38, meaning the sea state there is remarkably regular. The western Caribbean, by contrast, acts as a cyclone intensification zone where hurricanes passing over warm waters whip up episodic, extreme forcing. The Gulf of Mexico is dominated by a third mechanism entirely: seasonal cold fronts that sweep down from North America each winter, generating rough seas with a coefficient of variation of 0.54, the most variable of the three regimes.

With the climatology established, the study turned to the central question of trend detection. Detecting a genuine long-term trend in noisy geophysical data is notoriously difficult, and naive statistical tests can be fooled by autocorrelation, the tendency of consecutive years to resemble one another, and by heteroscedasticity, the tendency of variability to change over time. The analysis therefore applied Mann-Kendall trend tests with corrections for both effects, and then controlled the False Discovery Rate across thousands of grid points so that the handful of apparently significant results were not statistical flukes. The outcome was a sharp seasonal dichotomy. During the heart of the Atlantic hurricane season, the months of August, September and October, the 99th-percentile significant wave height, a robust index of extreme seas, shows significant increasing trends across 33.2 percent of the grid points in the northwestern Caribbean. Averaged over that region, the trend amounts to an increase of 0.254 meters per decade, with individual locations rising as fast as 0.606 meters per decade, results that remain significant at the p < 0.001 level after correction for multiple testing.

The winter months tell the opposite story. During November, December and January, when cold fronts rather than hurricanes dominate the wave climate, only 3.3 percent of grid points show any significant trend, a fraction consistent with pure statistical noise. In other words, the intensification of extreme waves is not a basin-wide drift but a seasonally and geographically concentrated phenomenon tied to the cyclone season, and specifically to the corner of the Caribbean where hurricanes most often pass through or intensify. This spatial and seasonal specificity is itself an important finding, because it rules out explanations that would require the entire basin to be changing uniformly, and it points instead at changes in the behavior or tracks of tropical cyclones.

The most dramatic result concerns a structural break in the record. Using the Pettitt change-point test, a non-parametric method for locating the moment when a time series shifts from one statistical regime to another, the analysis identifies a dominant break in 1995 or 1996 in the northwestern Caribbean. That timing is not arbitrary: the mid-1990s mark the transition of the Atlantic Multidecadal Oscillation into its warm phase, a well-documented shift in North Atlantic sea surface temperatures that has been linked in earlier research to the marked increase in Atlantic hurricane activity documented since 1995. The wave record, in effect, carries the fingerprint of that large-scale oceanic reorganization. Before the break, extreme waves in the region followed one statistical distribution; after it, they followed a demonstrably more severe one.

To quantify what that regime change means in practical terms, the study employed a peaks-over-threshold analysis, the workhorse of extreme-value statistics. Wave heights exceeding the local 99th-percentile threshold were extracted, with a seven-day declustering window applied so that the multiple large waves generated by a single storm count as one independent event rather than many. The exceedances were then fitted with a Generalised Pareto Distribution, from which the fifty-year return period wave height, the level expected to be exceeded once every fifty years on average, could be estimated for each period. In the cluster of 369 grid points identified by Local Indicators of Spatial Association analysis as a high-high hotspot of extreme wave change, the fifty-year return value rose from 5.90 meters in the 1979 to 1995 period to 9.81 meters in the 1996 to 2024 period, a change of 4.23 meters with a within-cluster spatial standard deviation of 1.84 meters. The shape parameter of the fitted distribution, which controls how heavy the tail of the distribution is, increased from 0.226 to 0.522, indicating that the most extreme events became not just more frequent but fundamentally more probable at very high levels.

Such a large jump invites skepticism, and the study addresses it directly. Because neighboring grid points in a reanalysis are not statistically independent, a spatial thinning sensitivity check was performed to confirm that the result survives when the effective sample size is reduced to account for spatial autocorrelation. It does. Meanwhile, the opposing low-low cluster, a region where extreme waves have become less severe, records a decrease of 1.98 meters with a standard deviation of 1.35 meters, a pattern consistent with the stability of the cold-front-driven regime. The contrast between the two clusters reinforces the interpretation that the change is real, spatially structured and physically meaningful rather than an artifact of the method.

The implications reach well beyond academic climatology. Ports, offshore energy installations, coastal highways and tourism infrastructure throughout the Gulf of Mexico and the Caribbean have historically been designed using return-period wave heights computed under the assumption of stationarity, the idea that the statistics of the past reliably describe the future. This study demonstrates that the assumption fails in the northwestern Caribbean: a structure designed in 1990 for a fifty-year wave of about six meters could now face seas approaching ten meters over its lifetime. Updated probabilistic design baselines of the kind this analysis provides are therefore not a luxury but a necessity for engineers and planners in the region, particularly as coastal populations and assets continue to grow along the hurricane-exposed shores of Mexico, Cuba and Central America.

Scientifically, the work adds a wave-based perspective to a debate that has mostly been conducted in terms of hurricane counts and wind speeds. Because significant wave height integrates the effect of storm intensity, duration and track within a single observable quantity, it offers an independent line of evidence on how tropical cyclone behavior has evolved. The alignment of the 1995-1996 break with the Atlantic Multidecadal Oscillation transition, the concentration of trends in the cyclone season, and the stability of the cold-front regime together paint a coherent picture: the extreme wave climate of this basin is governed by distinct forcing mechanisms, and it is the hurricane-driven one that has stepped into a new, more dangerous state. All data underlying the study are publicly available through the Copernicus Climate Data Store, allowing other researchers to scrutinize and extend a record that now documents nearly half a century of a changing sea.

Subject of Research: Long-term trends and regime changes in extreme significant wave height in the Gulf of Mexico and Caribbean Sea from 1979 to 2024

Article Title: Climatology, trends, and regime changes of extreme wave conditions in the Gulf of Mexico and Caribbean Sea (1979–2024)

Article References: Hidalgo-Mayo, A. (2026). Climatology, trends, and regime changes of extreme wave conditions in the Gulf of Mexico and Caribbean Sea (1979–2024). Theoretical and Applied Climatology, 157(10), Article 626. https://doi.org/10.1007/s00704-026-06564-6

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06564-6

Keywords: wave climate, significant wave height, Gulf of Mexico, Caribbean Sea, extreme value analysis, ERA5 reanalysis, tropical cyclones, Atlantic Multidecadal Oscillation, return periods, climate trends, Mann-Kendall test, coastal engineering

The study, published in the journal Ocean Dynamics, draws on continuous wave measurements recorded between 2017 and 2019 by the coastal moored buoy CB06, operated by the National Institute of Ocean Technology under India’s Ministry of Earth Sciences. The buoy sits in shallow water at a depth of just 16 metres, close enough to the shore that its readings are directly relevant to coastal engineering, port operations, erosion management and navigation. Rather than treating the measured waves as a single undifferentiated field, the researchers applied a wave steepness algorithm to each recorded wave spectrum, a technique that exploits the fundamental physical difference between young, steep wind seas and mature, low-steepness swells. By sorting the energy in every spectrum into these two categories, the team could track the significant wave height of the swell component and the significant wave height of the wind sea component separately, and follow how each evolved through three distinct seasonal windows: the pre-monsoon months of February through May, the southwest monsoon months of June through September, and the post-monsoon northeast monsoon months of October through January.

The technical logic behind the separation is deceptively simple. Wind seas, generated by local winds, tend to be relatively steep because the waves are still growing under active forcing; swells, having left their generation region behind, lose steepness as they disperse and travel. Steepness-based partitioning therefore acts as a physical fingerprinting tool, allowing each directional wave spectrum measured by the buoy to be sliced into a swell part and a wind sea part. From these partitions the researchers derived Hm0s, the significant wave height attributable to swells, and Hm0w, the significant wave height attributable to wind seas, and then examined interannual variations across the three-year record. This decomposition matters because the two components carry different information: wind seas signal what the local atmosphere is doing right now, while swells preserve a memory of winds that blew days earlier, sometimes on the other side of the basin.

The headline finding of the analysis is that swells dominate the wave field off Chennai. Across all three years, the total significant wave height, Hm0, correlated more strongly with the swell component than with the wind sea component, confirming that the character of the sea at this location is set primarily by long-period waves arriving from distant generation areas rather than by locally born wind waves. This is consistent with a broader understanding of the North Indian Ocean, where the wave climate along the Indian east coast is shaped substantially by swells propagating from the Southern Indian Ocean and from the Bay of Bengal itself. For coastal practitioners, the implication is significant: design conditions, sediment transport estimates and coastal flood assessments off Chennai cannot be built on local wind statistics alone, because the largest and most consistent share of wave energy arrives as swell.

What elevates the study beyond a climatological description is the year that sits at its centre. The 2017 to 2019 window happened to bracket a major climate event: in 2019, a strong El Niño-Southern Oscillation episode coincided with one of the strongest positive phases of the Indian Ocean Dipole on record, a coupled ocean-atmosphere pattern in which the western Indian Ocean becomes unusually warm relative to the east. These modes are known to reorganise winds and rainfall across the Indo-Pacific, but their fingerprints on the partitioned wave climate of the Bay of Bengal had been harder to pin down from direct measurements. The Chennai buoy record caught those fingerprints clearly.

During the 2019 southwest monsoon, the wind field over the study area showed an increased occurrence of winds blowing from between 180 and 270 degrees, a southwesterly bias consistent with the large-scale circulation anomalies that a strong positive Indian Ocean Dipole tends to impose on the region. More strikingly, during the pre-monsoon period of 2019, the researchers observed unusual southeasterly winds, a departure from the patterns seen in 2017 and 2018 that coincided with the evolving El Niño conditions. The wave record responded in kind. The anomalous southwesterly winds during the 2019 monsoon were accompanied by an increased occurrence of young swells, waves that had recently left their generation area and had not yet fully matured, alongside a reduction in the annual swell percentage. In other words, the reorganised wind field did not merely strengthen local waves; it altered the age and origin structure of the swell population itself.

The pre-monsoon season told the opposite story. As El Niño conditions developed, the study recorded more swell-dominated conditions during the pre-monsoon months, with the swell share of wave energy rising relative to the preceding years. The contrast between a windier, more wind-sea-rich monsoon and a swell-rich pre-monsoon in the same year illustrates how a single climate event can push the wave climate in different directions at different times of the year, depending on how it reshapes the regional wind field and the swell pathways feeding the coast. For the Chennai coast, this means that climate teleconnections are not an abstract background factor but an active modulator of the day-to-day wave conditions that beaches, breakwaters and fishing communities actually experience.

The most quantitatively dramatic result concerns the wind sea component during the 2019 monsoon. The occurrence of wind sea significant wave heights exceeding 0.5 metres increased by 25 percent relative to 2017 and by 24 percent relative to 2018. In a shallow 16-metre water column, wind seas of that scale are far from trivial: they contribute directly to nearshore turbulence, sediment stirring and the wave-induced stresses that drive coastal erosion, a persistent problem along the Chennai shoreline. A quarter-century-scale jump in the frequency of such conditions within a single anomalous year demonstrates how quickly the shallow-water wave regime can shift under the influence of basin-scale climate variability, and how important it is for coastal models and operational forecasting systems to account for interannual climate modes rather than relying solely on a mean seasonal climatology.

The study also adds to a growing body of work showing that the Indian Ocean’s wave climate is tightly coupled to its leading climate modes, including ENSO and the Indian Ocean Dipole, which modulate wind patterns, swell generation and wave propagation pathways across the basin. Earlier research has linked these modes to wave climate variability in the eastern Arabian Sea and to high-swell events along the Indian coast, but direct, partitioned measurements from a shallow-water buoy off the east coast provide a particularly vivid confirmation. Because the data come from a long-running, quality-controlled moored buoy network maintained by the National Institute of Ocean Technology, the record offers the kind of continuous, in situ validation that satellite altimeters and numerical wave models alone cannot always provide in the complex nearshore environment.

The practical consequences reach well beyond academic interest. Chennai is a major port city with dense coastal infrastructure, an eroding shoreline and a large population exposed to marine hazards. Wave climate information that distinguishes swells from wind seas directly improves the inputs to shoreline change models, breakwater design criteria, sediment budget studies and navigational safety assessments. The finding that a strong positive Indian Ocean Dipole year can simultaneously boost wind sea occurrences during the monsoon and swell dominance before it suggests that seasonal and interannual wave forecasts tailored to climate mode outlooks could become valuable tools for coastal managers. As climate variability and change continue to reshape the Indian Ocean’s winds and waves, the humble buoy off Chennai, watching the sea separate its local storms from its far-travelled swells, is helping to write the baseline against which those future changes will be measured.

Subject of Research: Wind sea and swell partitioning in the shallow-water wave climate off Chennai and its modulation by ENSO and the Indian Ocean Dipole

Article Title: Wind sea and swell characteristics in the wave climate off Chennai

Article References: Janakiram, R., Latha, G., Balamurugan, R., & Jena, B. K. (2026). Wind sea and swell characteristics in the wave climate off Chennai. Ocean Dynamics, 76(10), Article 99. https://doi.org/10.1007/s10236-026-01856-x

Image Credits: AI Generated

DOI: 10.1007/s10236-026-01856-x

Keywords: moored buoy, wind seas, swell, wave climate, wave spectra, Bay of Bengal, ENSO, Indian Ocean Dipole, significant wave height, Chennai coast, monsoon, coastal oceanography